No abstract

As seismic data can contain information over a large spatial area and are sensitive to changes in the properties of the subsurface, seismic imaging has become the standard geophysical monitoring method for many applications such as Carbon capture and storage and reservoir monitoring. The availability of practical tools such as full-waveform inversion (FWI) makes time-lapse seismic FWI a promising method for monitoring subsurface changes. However, FWI is a highly ill-posed problem thatcan generate artifacts. Because the changes in the earth's properties are typically small in terms of magnitude and spatial extent, discriminating the true time-lapse signature from noise can be challenging. Different strategies have been proposed to address these difficulties. In this study, we propose a weighted-average inversion to better control the effects of artifacts and differentiate them from the true 4D changes. We further compare ve different related strategies with synthetic tests on clean and noisy data. The effects of seawater-velocity variation on different strategies are also studied as one of the main sources of nonrepeatability. We tested different strategies of time-lapse FWI using the Marmousi and the SEAM Time-Lapse models. The results indicate that the weighted-average method can provide the best compromise between accuracy and computation time. This method also provides a range for the possible answer of other time-lapse FWI strategies.

No abstract

No abstract

No abstract